Compared to economic migrants, refugees usually have less choice over their destination and less time to prepare before they leave; consequently, they are confronted with a larger mismatch in the labor market. This is reinforced by refugee and asylum-seeker settlement policies that aim at spatial dispersal and do not take labor markets into account.

This is why I was pleased to discuss a proposal at the Center for Global Development that aims to settle refugees in a manner that increases their employment rates. Researchers from the Immigration Policy Lab (IPL) at Stanford University and ETH Zurich have developed a data-driven algorithm that examines potential synergies between refugee characteristics and geographical locations in a systematic way. Their data show that the expected employment returns for certain characteristics (like speaking the local language, being from a certain country or having a certain level of education) can vary across different locations. In running the algorithm using historical data from the US and Switzerland, they find that a better matching process can increase average employment rates by 41 percent in the US and 73 percent in Switzerland. Other interventions like language classes or trainings have a much lower return on investment.

The algorithm could incorporate refugees’ individual constraints (like medical needs) as well as preferences, if the data are available. It would be important to include refugee preferences—such as, whether they seek to live with family; otherwise they may choose to move, which would lower incentives to integrate into the labor market in the location where they were settled. The algorithm could also optimize other variables like the quality of employment. It can be regularly updated with new data as the synergies between certain refugee characteristics and geographical places change.

The algorithm developed by researchers at IPL is a notable example of harnessing big data and machine learning for tangible improvements for refugees. Even if we would like to know why certain refugees are faring better in some places than in others, we do not need to search for such answers: The algorithm can produce results that improve their employment outcomes. In principle, the algorithm could be used not only for resettlement but also in countries of first asylum within and outside the OECD, where similar systems of spatial dispersal also exist. An example is Turkey, where conditional refugees (i.e. non-European refugees that would fall under the Geneva convention) are assigned to one of the 62 satellite cities (which exclude Ankara, Istanbul and Izmir). In Germany newly arrived asylum seekers are distributed across Federal States mainly based on a quota system (the Koenigsstein key).

Using the algorithm would produce results different than dispersal policies based primarily on the goal—like limitations on the right to work—of reducing negative impacts on the host population.

A challenge, particularly in non-OECD countries, will be the lack of data. A pilot of the algorithm might also be confronted with other practical and political barriers. Policy makers at the local level might be concerned when their municipalities are allocated asylum seekers and refugees according to an algorithm—even if the data for Switzerland and the U.S. show that such placements would lead to higher employment rates in almost every location. To surmount this barrier, the algorithm could make sure that the refugee employment rate in each location is at least the same as it has been in the past (with settlement without the algorithm), even if this would decrease the overall gains in terms of refugee employment on the country level. And of course the algorithm will also only be useful where host countries permit refugees to work.

Using the algorithm would produce results different than dispersal policies based primarily on the goal—like limitations on the right to work—of reducing negative impacts on the host population. Policies that disperse refugees and settle them in remote areas might reduce negative impacts on the host population in the short term; but they tend to increase negative impacts in the longer term, as they make it even more difficult for refugees to integrate and become productive members of society.

So far, the algorithm has been showcased to improve these existing placement systems within a country, which aim at geographical dispersal and limit the number of refugees that can be placed in each location. It improves employment outcomes within these limitations. The algorithm could, however, also help inform a political discussion about the economic costs of dispersal policies by calculating the costs of limiting the number of refugees that can be settled in each location and comparing them to the benefits of a settlement, where there a higher limit, or no limit, on the number of refugees that can be placed in each location and each refugee can be placed where he or she has the highest probability of employment. Even if the considerations for the admission of refugees are not and should not be economic, it is important to understand how restrictions on their place of residence might further hamper their integration into the labor market.

Kirsten Schuettleris a Senior Program Officer in Global Knowledge Partnership on Migration and Development (KNOMAD) in the Jobs Group, Social Protection and Jobs Global Practice of the World Bank.